The Net Outward Investment Position (NOIP) indicator is insufficient for the purposes of understanding firms’ internationalization decision-making behaviour. The indicator does not allow for the withdrawal of insights into the structure of an economy, and is a weak predictor of the degree of Foreign Direct Investment (FDI). We argue that a typology of firms aggregated according to intrinsic characteristics of those firms is a better predictor of the degree of internationalization of an economy than the NOIP. We use a database of 2133 firms based in Portugal that are already internationalized, made available in AICEP, a government agency. We use multiple correspondence and cluster analyses to build a typology of firms, and obtain evidence of common characteristics of the constituent groups. We identify a typology of firms characterized by five types differentiated by firm age, length of internationalization process, sector of economic activity, legal status, and psychological/cultural proximity. These variables suggest an evolutionary, iterative, self-learning approach to internationalization, which can be better explained by the combined use of the Investment Development Path (IDP) framework, the Uppsala Evolutionary School, and Vernon´s Product Life Cycle theory. Additionally, we find that the most striking differences between developed and developing host countries are in terms of the economic sector, legal status of the firm, and belonging (or not) to an economic group. We establish a link between the IDP framework, the Uppsala Evolutionary school, and Vernon´s Product Life Cycle theory, using a categorization of firms made according to selected characteristics to understand the internationalization of firms.

The authors would like to thank the National Science Foundation (FCT), through projects UIDB/00315/2020 and UIBD/03126/2020, AICEP - Portugal Global for providing the data, the editor, and two anonymous referees. The usual disclaimer applies.

Internationalization theories,Multiple correspondence analysis,Cluster analysis,Portugal

• Economia e Gestão - Ciências Sociais